EMDB: The Electromagnetic Database of Global 3D Human Pose and Shape in the Wild

We present EMDB, the Electromagnetic Database of Global 3D Human Pose and Shape in the Wild. EMDB is a novel dataset that contains high-quality 3D SMPL pose and shape parameters with global body and camera trajectories for in-the-wild videos. We use body-worn, wireless electromagnetic (EM) sensors and a hand-held iPhone to record a total of 58 minutes of motion data, distributed over 81 indoor and outdoor sequences and 10 participants. Together with accurate body poses and shapes, we also provide global camera poses and body root trajectories. To construct EMDB, we propose a multi-stage optimization procedure, which first fits SMPL to the 6-DoF EM measurements and then refines the poses via image observations. To achieve high-quality results, we leverage a neural implicit avatar model to reconstruct detailed human surface geometry and appearance, which allows for improved alignment and smoothness via a dense pixel-level objective. Our evaluations, conducted with a multi-view volumetric capture system, indicate that EMDB has an expected accuracy of 2.3 cm positional and 10.6 degrees angular error, surpassing the accuracy of previous in-the-wild datasets. We evaluate existing state-of-the-art monocular RGB methods for camera-relative and global pose estimation on EMDB. EMDB is publicly available under https://ait.ethz.ch/emdbComment: Accepted to ICCV 202

The United States COVID-19 Forecast Hub dataset

Academic researchers, government agencies, industry groups, and individuals have produced forecasts at an unprecedented scale during the COVID-19 pandemic. To leverage these forecasts, the United States Centers for Disease Control and Prevention (CDC) partnered with an academic research lab at the University of Massachusetts Amherst to create the US COVID-19 Forecast Hub. Launched in April 2020, the Forecast Hub is a dataset with point and probabilistic forecasts of incident cases, incident hospitalizations, incident deaths, and cumulative deaths due to COVID-19 at county, state, and national, levels in the United States. Included forecasts represent a variety of modeling approaches, data sources, and assumptions regarding the spread of COVID-19. The goal of this dataset is to establish a standardized and comparable set of short-term forecasts from modeling teams. These data can be used to develop ensemble models, communicate forecasts to the public, create visualizations, compare models, and inform policies regarding COVID-19 mitigation. These open-source data are available via download from GitHub, through an online API, and through R packages

Pricing Efficiency of China's Exchange-Traded Fund Market

This study investigates the pricing efficiency of the Shanghai 50 ETF (SSE 50 ETF), the first exchange-traded fund (ETF) in China. The empirical results demonstrate that ETF market prices and net asset values (NAV) are cointegrated and there is unidirectional causality from price to NAV. The conditional variance dynamics from the augmented Generated AutoRegressive Conditional Heteroskedasticity (GARCH) framework show that ETF market prices influence NAV volatility and therefore can be used as price-discovery vehicles. The study also finds that the fund's prices did not closely follow the NAV during the second half of 2007, when the Chinese stock market experienced substantial volatility, reflecting sudden increased market risks as well as potential arbitrage opportunities during financial turbulences.

Timing-driven placement for carbon nanotube circuits

© 2015 IEEE. Carbon nanotube field effect transistors (CNFETs), which use carbon nanotubes (CNTs) as the transistor channel, are promising substitution of conventional CMOS technology. However, due to the stochastic assembly process of CNTs, the number of CNTs in each CNFET has a large variation, resulting in a vast circuit delay variation and timing yield degradation. To overcome it, we propose a timing-driven placement method for CNFET circuits. It exploits a unique feature of CNFET circuits, namely, asymmetric spatial correlation: CNFETs that lie along the CNT growth direction are highly correlated in terms of their electrical properties. Our method distributes CNFETs of the same critical paths to different rows perpendicular to the CNT growth direction during both global and detailed placement phases, while optimizing the timing of these critical paths. Experimental results demonstrated that our approach reduces both the mean and the variance of circuit delay, leading to an improvement in timing yield

gDNA: Towards Generative Detailed Human Avatars

To make 3D human avatars widely available, we must be able to generate a variety of 3D virtual humans with varied identities and shapes in arbitrary poses. This task is chal-lenging due to the diversity of clothed body shapes, their complex articulations, and the resulting rich, yet stochas-tic geometric detail in clothing. Hence, current methods that represent 3D people do not provide a full generative model of people in clothing. In this paper, we propose a novel method that learns to generate detailed 3D shapes of people in a variety of garments with corresponding skin-ning weights. Specifically, we devise a multi-subject forward skinning module that is learned from only a few posed, unrigged scans per subject. To capture the stochastic nature of high-frequency details in garments, we leverage an adversarial loss formulation that encourages the model to capture the underlying statistics. We provide empirical evi-dence that this leads to realistic generation of local details such as wrinkles. We show that our model is able to gen-erate natural human avatars wearing diverse and detailed clothing. Furthermore, we show that our method can be used on the task of fitting human models to raw scans, out-performing the previous state-of-the-art

Non-native fish of the Upper Irtysh and the Ulungur Rivers in China

The Chinese section of the Irtysh River Basin and the Ulungur River Basin, two major river basins of the Altay region, are located at the northwest of Xinjiang Uygur Autonomous Region of China. As an international river, the Chinese section has seven state-level protected fish and seven local-level protected species as well. Many more commercial species have been introduced from eastern China and other countries, accompanied by some low-value and small-sized fish in recent decades. The non-native fish species have already threatened these protected fish. This study investigated the distribution of non-native fish species in the Chinese section of the Irtysh River Basin and the Ulungur River Basin. The basic data for the biodiversity conservation and the information of the non-native fish in these two river basins were gathered.There are a lot of studies on native fish in the Chinese section of the Irtysh River Basin and Urungur River Basin in China, but there is a lack of studies on non-native fish. Thirteen non-native fish belonging to four orders, nine families and 12 genera were collected in this study. The study includes one dataset. The dataset presents taxonomy, distribution, water body and location for each of the non-native fish collected from the Chinese section of the Irtysh River Basin and the Ulungur River Basin. Our study has found that the proportion of native species has declined, while the number of non-native species has increased from 2013 to 2022. The information we provided could help to develop an international strategy for the protection of aquatic biodiversity

Non-native fish of the Upper Irtysh and the Ulungur Rivers in China

The Chinese section of the Irtysh River Basin and the Ulungur River Basin, two major river basins of the Altay region, are located at the northwest of Xinjiang Uygur Autonomous Region of China. As an international river, the Chinese section has seven state-level protected fish and seven local-level protected species as well. Many more commercial species have been introduced from eastern China and other countries, accompanied by some low-value and small-sized fish in recent decades. The non-native fish species have already threatened these protected fish. This study investigated the distribution of non-native fish species in the Chinese section of the Irtysh River Basin and the Ulungur River Basin. The basic data for the biodiversity conservation and the information of the non-native fish in these two river basins were gathered.There are a lot of studies on native fish in the Chinese section of the Irtysh River Basin and Urungur River Basin in China, but there is a lack of studies on non-native fish. Thirteen non-native fish belonging to four orders, nine families and 12 genera were collected in this study. The study includes one dataset. The dataset presents taxonomy, distribution, water body and location for each of the non-native fish collected from the Chinese section of the Irtysh River Basin and the Ulungur River Basin. Our study has found that the proportion of native species has declined, while the number of non-native species has increased from 2013 to 2022. The information we provided could help to develop an international strategy for the protection of aquatic biodiversity